A Model of Understanding in Deep Learning Systems

• URL: http://arxiv.org/abs/2604.04171v1
• Date: Sun, 05 Apr 2026 16:27:43 GMT
• Title: A Model of Understanding in Deep Learning Systems
• Abstract summary: I propose a model of systematic understanding, suitable for machine learning systems.<n>An agent understands a property of a target system when it contains an adequate internal model that tracks real regularities, is coupled to the target by stable bridge principles, and supports reliable prediction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: I propose a model of systematic understanding, suitable for machine learning systems. On this account, an agent understands a property of a target system when it contains an adequate internal model that tracks real regularities, is coupled to the target by stable bridge principles, and supports reliable prediction. I argue that contemporary deep learning systems often can and do achieve such understanding. However they generally fall short of the ideal of scientific understanding: the understanding is symbolically misaligned with the target system, not explicitly reductive, and only weakly unifying. I label this the Fractured Understanding Hypothesis.

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